System and method for client network congestion detection, analysis, and management

A method for automated detection of a congestion incident from real-time network in a network, the method comprising:

• collecting real-time network data;
• executing calculations on the real-time network data to compute performance metric;
• determining performance metrics from the executed calculations on the real-time network data;
• determining peak usage time;
• detecting one or more congestion incidents, wherein a congestion incident comprises a persistence of one or more metrics over a time window that comprises detecting a proportion of metric values crossing a threshold that exceeds a defined percentage amount, detecting a time-ordered stretch of metric values with a length of the time-ordered stretch exceeding a defined threshold, or combinations thereof;
• determining one or more outlying subgroups within the detected network congestion incident;
• and
• identifying root causes of the one or more congestion incidents and the one or more outlying subgroups.

A method for automated detection of a congestion incident from real-time network in a network, the method comprising:

• collecting real-time network data;
• executing calculations on the real-time network data to compute performance metric;
• determining performance metrics from the executed calculations on the real-time network data;
• determining peak usage time;
• detecting one or more congestion incidents, wherein a congestion incident comprises a persistence of one or more metrics over a time window that comprises detecting a proportion of metric values crossing a threshold that exceeds a defined percentage amount, detecting a time-ordered stretch of metric values with a length of the time-ordered stretch exceeding a defined threshold, or combinations thereof;
• determining one or more outlying subgroups within the detected network congestion incident;
• identifying root causes of the one or more congestion incidents and the one or more outlying subgroups;
• and
• predicting performance issues and identifying network capacity upgrades based on the one or more congestion incidents.